"""
Retrain the YOLO model for your own dataset.
"""

import numpy as np
import keras.backend as K
from keras.layers import Input, Lambda
from keras.models import Model
from keras.optimizers import Adam
from keras.callbacks import (
    TensorBoard,
    ModelCheckpoint,
    ReduceLROnPlateau,
    EarlyStopping,
)

from yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss
from yolo3.utils import get_random_data


def _main():
    annotation_path = "data_train.txt"
    log_dir = "logs/003/"
    classes_path = "data_classes.txt"
    # anchors_path = 'model_data/yolo-tiny_anchors.txt'
    anchors_path = "model_data/yolo_anchors.txt"
    class_names = get_classes(classes_path)
    num_classes = len(class_names)
    anchors = get_anchors(anchors_path)

    input_shape = (416, 416)  # multiple of 32, hw
    epoch1, epoch2 = 40, 40

    is_tiny_version = len(anchors) == 6  # default setting
    if is_tiny_version:
        model = create_tiny_model(
            input_shape,
            anchors,
            num_classes,
            freeze_body=2,
            weights_path="model_data/yolo-tiny.h5",
        )
    else:
        model = create_model(
            input_shape,
            anchors,
            num_classes,
            freeze_body=2,
            weights_path="model_data/yolo.h5",
        )  # make sure you know what you freeze

    logging = TensorBoard(log_dir=log_dir)
    # checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',
    #     monitor='val_loss', save_weights_only=True, save_best_only=True, period=3)
    checkpoint = ModelCheckpoint(
        log_dir + "checkpoint.h5",
        monitor="val_loss",
        save_weights_only=True,
        save_best_only=True,
        period=5,
    )
    reduce_lr = ReduceLROnPlateau(monitor="val_loss", factor=0.1, patience=3, verbose=1)
    early_stopping = EarlyStopping(
        monitor="val_loss", min_delta=0, patience=10, verbose=1
    )

    val_split = 0.1
    with open(annotation_path) as f:
        lines = f.readlines()
    np.random.seed(10101)
    np.random.shuffle(lines)
    np.random.seed(None)
    num_val = int(len(lines) * val_split)
    num_train = len(lines) - num_val

    # Train with frozen layers first, to get a stable loss.
    # Adjust num epochs to your dataset. This step is enough to obtain a not bad model.
    if True:
        model.compile(
            optimizer=Adam(lr=1e-3),
            loss={
                # use custom yolo_loss Lambda layer.
                "yolo_loss": lambda y_true, y_pred: y_pred
            },
        )

        batch_size = 32
        print(
            "Train on {} samples, val on {} samples, with batch size {}.".format(
                num_train, num_val, batch_size
            )
        )
        model.fit_generator(
            data_generator_wrapper(
                lines[:num_train], batch_size, input_shape, anchors, num_classes
            ),
            steps_per_epoch=max(1, num_train // batch_size),
            validation_data=data_generator_wrapper(
                lines[num_train:], batch_size, input_shape, anchors, num_classes
            ),
            validation_steps=max(1, num_val // batch_size),
            epochs=epoch1,
            initial_epoch=0,
            callbacks=[logging, checkpoint],
        )
        model.save_weights(log_dir + "trained_weights_stage_1.h5")

    # Unfreeze and continue training, to fine-tune.
    # Train longer if the result is not good.
    if True:
        for i in range(len(model.layers)):
            model.layers[i].trainable = True
        model.compile(
            optimizer=Adam(lr=1e-4), loss={"yolo_loss": lambda y_true, y_pred: y_pred}
        )  # recompile to apply the change
        print("Unfreeze all of the layers.")

        batch_size = (
            16  # note that more GPU memory is required after unfreezing the body
        )
        print(
            "Train on {} samples, val on {} samples, with batch size {}.".format(
                num_train, num_val, batch_size
            )
        )
        model.fit_generator(
            data_generator_wrapper(
                lines[:num_train], batch_size, input_shape, anchors, num_classes
            ),
            steps_per_epoch=max(1, num_train // batch_size),
            validation_data=data_generator_wrapper(
                lines[num_train:], batch_size, input_shape, anchors, num_classes
            ),
            validation_steps=max(1, num_val // batch_size),
            epochs=epoch1 + epoch2,
            initial_epoch=epoch1,
            callbacks=[logging, checkpoint, reduce_lr, early_stopping],
        )
        model.save_weights(log_dir + "trained_weights_final.h5")

    # Further training if needed.


def get_classes(classes_path):
    """loads the classes"""
    with open(classes_path) as f:
        class_names = f.readlines()
    class_names = [c.strip() for c in class_names]
    return class_names


def get_anchors(anchors_path):
    """loads the anchors from a file"""
    with open(anchors_path) as f:
        anchors = f.readline()
    anchors = [float(x) for x in anchors.split(",")]
    return np.array(anchors).reshape(-1, 2)


def create_model(
    input_shape,
    anchors,
    num_classes,
    load_pretrained=True,
    freeze_body=2,
    weights_path="model_data/yolo_weights.h5",
):
    """create the training model"""
    K.clear_session()  # get a new session
    image_input = Input(shape=(None, None, 3))
    h, w = input_shape
    num_anchors = len(anchors)

    y_true = [
        Input(
            shape=(
                h // {0: 32, 1: 16, 2: 8}[l],
                w // {0: 32, 1: 16, 2: 8}[l],
                num_anchors // 3,
                num_classes + 5,
            )
        )
        for l in range(3)
    ]

    model_body = yolo_body(image_input, num_anchors // 3, num_classes)
    print(
        "Create YOLOv3 model with {} anchors and {} classes.".format(
            num_anchors, num_classes
        )
    )

    if load_pretrained:
        model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
        print("Load weights {}.".format(weights_path))
        if freeze_body in [1, 2]:
            # Freeze darknet53 body or freeze all but 3 output layers.
            num = (185, len(model_body.layers) - 3)[freeze_body - 1]
            for i in range(num):
                model_body.layers[i].trainable = False
            print(
                "Freeze the first {} layers of total {} layers.".format(
                    num, len(model_body.layers)
                )
            )

    model_loss = Lambda(
        yolo_loss,
        output_shape=(1,),
        name="yolo_loss",
        arguments={
            "anchors": anchors,
            "num_classes": num_classes,
            "ignore_thresh": 0.5,
        },
    )([*model_body.output, *y_true])
    model = Model([model_body.input, *y_true], model_loss)

    return model


def create_tiny_model(
    input_shape,
    anchors,
    num_classes,
    load_pretrained=True,
    freeze_body=2,
    weights_path="model_data/tiny_yolo_weights.h5",
):
    """create the training model, for Tiny YOLOv3"""
    K.clear_session()  # get a new session
    image_input = Input(shape=(None, None, 3))
    h, w = input_shape
    num_anchors = len(anchors)

    y_true = [
        Input(
            shape=(
                h // {0: 32, 1: 16}[l],
                w // {0: 32, 1: 16}[l],
                num_anchors // 2,
                num_classes + 5,
            )
        )
        for l in range(2)
    ]

    model_body = tiny_yolo_body(image_input, num_anchors // 2, num_classes)
    print(
        "Create Tiny YOLOv3 model with {} anchors and {} classes.".format(
            num_anchors, num_classes
        )
    )

    if load_pretrained:
        model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)
        print("Load weights {}.".format(weights_path))
        if freeze_body in [1, 2]:
            # Freeze the darknet body or freeze all but 2 output layers.
            num = (20, len(model_body.layers) - 2)[freeze_body - 1]
            for i in range(num):
                model_body.layers[i].trainable = False
            print(
                "Freeze the first {} layers of total {} layers.".format(
                    num, len(model_body.layers)
                )
            )

    model_loss = Lambda(
        yolo_loss,
        output_shape=(1,),
        name="yolo_loss",
        arguments={
            "anchors": anchors,
            "num_classes": num_classes,
            "ignore_thresh": 0.7,
        },
    )([*model_body.output, *y_true])
    model = Model([model_body.input, *y_true], model_loss)

    return model


def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes):
    """data generator for fit_generator"""
    n = len(annotation_lines)
    i = 0
    while True:
        image_data = []
        box_data = []
        for b in range(batch_size):
            if i == 0:
                np.random.shuffle(annotation_lines)
            image, box = get_random_data(annotation_lines[i], input_shape, random=True)
            image_data.append(image)
            box_data.append(box)
            i = (i + 1) % n
        image_data = np.array(image_data)
        box_data = np.array(box_data)
        y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)
        yield [image_data, *y_true], np.zeros(batch_size)


def data_generator_wrapper(
    annotation_lines, batch_size, input_shape, anchors, num_classes
):
    n = len(annotation_lines)
    if n == 0 or batch_size <= 0:
        return None
    return data_generator(
        annotation_lines, batch_size, input_shape, anchors, num_classes
    )


if __name__ == "__main__":
    _main()
